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Cloud Analytics

What is Cloud Analytics?

With cloud analytics, data is stored, processed, or both in the cloud, allowing customers to analyze their data and form actionable insights from patterns that emerge.

How is Cloud Analytics Different From Regular Data Analytics?

There isn’t much that differentiates cloud analytics from regular data analytics with the exception of where the data might be stored and/or processed. Cloud analytics can oftentimes be more flexible, accessible to more team members, and, oftentimes, more budget-friendly.

How Does Cloud Analytics Work? 

Because cloud analytics uses cloud-based resources to collect, process, analyze, and visualize data for an organization. Data is first gathered from various sources and stored in the cloud. Next it can be processed to be prepared for analysis, using data integration, cleansing, and transformation methods. During analysis, cloud-based tools can be used to identify any patterns or trends and uncover insights in the available data. Finally, this information can be communicated internally or externally using data visualization – dashboards, charts, graphs, and other visual methods of conveying information. Simplifying the data allows leadership to make decisions based on what is being presented.

One of the major benefits of cloud analytics is in how it works. Because processes can be conducted using cloud-based tools, organizations can rely less on expensive hardware and software.

The 6 Elements of Cloud Analytics

Gartner defined six elements of cloud analytics: data sources, data models, processing applications, computing power, analytics models, and sharing / storage of results.

Data Sources

These can include any original sources of data that might be used in analytics. This data can come from website usage, social media, ERPs, CRMs, practice management software, or elsewhere.

Data Models

Data models determine the relationship between data points, often with structured data types.

Processing Applications

As data goes into a warehouse, it needs to be processed by an application. Snowflake, Dataproc, Hadoop, and BigQuery are all examples of big data processing applications.

Computing Power

Computing power needs to scale with the volume of data to help along every step of the analytics process (ingesting, structuring, cleaning, analyzing, serving)

Analytics Models

Mathematical models used in cloud analytics require a lot of computing power and can be used to predict outcomes from data.

Sharing / Storage of Results

The results of your data processing and analyzing need to be able to be stored and shared. Understand how cloud storage and sharing works at data warehouses with your cloud providers.

Types of Cloud Analytics Infrastructure

The infrastructure available for cloud analytics is the same as it is for any cloud-based applications or processes. When it comes to cloud platforms, organizations have the choice between public cloud, private cloud, and hybrid or multicloud solutions.

Public Cloud

Public cloud is generally the most flexible and cost-effective, but the least customizable. Companies are able to scale up and down as needed with a combination of pay-as-you-go and long-term bulk licensing agreements. In this multitenant environment, resources are shared with other organizations, while data is kept proprietary. Some examples of public cloud providers include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Public cloud platforms offer a wide range of data analytics capabilities that help organizations process and analyze large volumes of data, make data-driven decisions, and gain insights into their business operations. Key tools available to organizations on public cloud platforms include:

  • Data storage: Amazon S3, Azure Blob Storage, Google Cloud Storage
  • Data processing: Amazon EMR, Azure HDInsight, Google Cloud Dataproc
  • Data warehousing: Redshift, Azure Synapse Analytics, Google BigQuery
  • Data analytics: Amazon Athena, Azure Stream Analytics, Google Cloud Dataflow
  • Machine learning: Amazon SageMaker, Azure Machine Learning, Google Cloud AI Platform
  • Business Intelligence: Amazon QuickSight, Azure Power BI, Looker Studio

Private Cloud

A private cloud is just what it sounds like - a cloud where systems and data are private to one company. This solution is best for organizations that have highly specific needs for their data center or data security concerns for sensitive information over and above what might be covered in a public cloud environment (special compliance considerations, extra-sensitive publicly identifiable information, etc.).

Organizations can have increased security and control over their data and greater flexibility for customization and improved performance due to lower latency and faster data transfer. However, this can also require a greater upfront investment in hardware and software, plus ongoing maintenance and management costs.

Data analytics capabilities in the private cloud are similar to those found in public cloud platforms. The key difference is that private clouds are dedicated to a single organization and are typically located within the organization’s own data center or on-premises infrastructure. Key data analytics tools in the private cloud include:

  • Data storage: VMware, vSAN, OpenStack Swift, Microsoft Azure Stack
  • Data processing: Apache Hadoop, Apache Spark, Microsoft HDInsight
  • Data warehousing: Oracle Exadata, Microsoft SQL Server, IBM Netezza.
  • Data analytics: Apache Zeppelin, Microsoft Power BI, IBM Cognos Analytics
  • Machine learning: IBM Watson Machine Learning, Microsoft Azure Machine Learning, Google Cloud AutoML
  • Business intelligence: Microsoft Power BI, IBM Cognos Analytics, Tableau Server

Hybrid or Multicloud

Sitting at the intersection between public and private cloud is hybrid cloud or multicloud environments. While similar, there is a distinct difference between the two. Hybrid cloud environments include a mix of public and private clouds and can also include on-premises frameworks, whereas hybrid cloud architecture only includes a mix of cloud environments. This can serve as a happy medium for organizations that need extra coverage for extremely sensitive data but want to enjoy cost savings from other data with on-demand services.
Hybrid cloud models combine the benefits of both public and private clouds, offering organizations greater flexibility and control over their data and applications. The benefits of hybrid cloud analytics include:

  • Scalability with Security: Hybrid cloud models offer the scalability of public clouds, allowing organizations to scale up or down their compute and storage resources as needed, while also offering the security and control of private clouds. Robust security features and compliance certifications protect data and ensure regulatory compliance.
  • Cost-effectiveness: Organizations can leverage pay-as-you-go pricing models and avoid the upfront costs of investing in on-premises infrastructure in the public cloud, while also offering the flexibility to run critical workloads in a secure private cloud environment.
  • Agility: Hybrid cloud models offer the agility of public clouds, allowing organizations to quickly respond to changing business needs by leveraging cloud analytics to gain new insights into their data and optimize processes.
  • Flexibility: Hybrid cloud models offer greater flexibility in terms of workload placement and data storage. Organizations can choose to run workloads in either public or private cloud environments, depending on their specific requirements, while also having the ability to move workloads and data between environments as needed.
  • Disaster Recovery: Hybrid cloud models offer the ability to leverage public cloud services for disaster recovery and business continuity, while also providing the security and control of private cloud environments for mission-critical workloads.

What Are The Benefits of Cloud Analytics?

By employing cloud analytics, organizations can gain increased insight by easily sharing and collaborating findings with others, save money, improve their security posture, and continue to scale their processes as necessary.

Increased Insights

Because the cloud allows for easy sharing and collaboration, data can be analyzed from multiple members of the team. While one department may not uncover insights from a set of data, another that has been given access might, helping you move forward and find opportunities you might have previously missed.

Cost Efficiencies

Cloud analytics is at its most cost-effective when businesses sign up for public cloud services at longer-term contracts (1-year and 3-year commitments). These licenses come with significant savings, oftentimes cutting bills in half or more. For remaining public cloud needs, there are options that allow organizations to pay only for what they use. Both of these pricing models can save money compared to private cloud and on-premises models, and with both, you won’t have to worry about paying for new infrastructure, software upgrades, or installations.

Improved Security Posture

Whether public, private, or hybrid, cloud technologies are often more secure than on-premise architecture, leaving cloud analytics to be a more secure alternative. Organizations also have the power to choose the level of data protection and compliance measures needed for the data by employing the appropriate cloud configurations. Security is also easier to manage via cloud because of increased visibility over the environment.


Because you’re able to buy resources as you need them, scaling up your cloud analytics can be a quick and simple process. By hosting analytics in the cloud, organizations are able to achieve rightsizing with minimal effort.

How TierPoint Can Enhance Your Cloud Analytics Goals?

When it comes time to invest in cloud analytics as part of your cloud-enhanced digital transformation strategy, it can be hard to identify the right first step. Luckily, TierPoint offers data analytics consulting services for all cloud configurations that can help you find and capture the hidden knowledge that currently lives in your data. With our assistance, you can use that knowledge to become more decisive and uncover new ways to do business. With data analysis platforms and AI in the public cloud, your business will be able to achieve better business outcomes. We can help get you there.

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